Chatbot conversacional de IA con Transformers en Python

Aprenda a usar la biblioteca de transformadores Huggingface para generar respuestas conversacionales con el modelo DialoGPT previamente entrenado en Python.

Los chatbots han ganado mucha popularidad en los últimos años y, a medida que crece el interés en el uso de chatbots para empresas, los investigadores también hicieron un gran trabajo en el avance de los chatbots de IA conversacionales.

En este tutorial, usaremos la biblioteca de transformadores Huggingface para emplear el modelo DialoGPT previamente entrenado para la generación de respuestas conversacionales.

DialoGPT es un modelo de generación de respuesta conversacional neuronal sintonizable a gran escala que se entrenó en 147 millones de conversaciones extraídas de Reddit, y lo bueno es que puede ajustarlo con su conjunto de datos para lograr un mejor rendimiento que el entrenamiento desde cero.

Para comenzar, instalemos transformadores :

$ pip3 install transformers

Abra un nuevo archivo o cuaderno de Python y haga lo siguiente:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# model_name = "microsoft/DialoGPT-large"
model_name = "microsoft/DialoGPT-medium"
# model_name = "microsoft/DialoGPT-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Hay tres versiones de DialoGPT; pequeño, mediano y grande. Por supuesto, cuanto más grande, mejor, pero si ejecuta esto en su máquina, creo que el tamaño pequeño o mediano se adapta a su memoria sin problemas. También puede utilizar Google Colab para probar el más grande.

Generación de respuestas con búsqueda codiciosa

En esta sección, usaremos el algoritmo de búsqueda codiciosa para generar respuestas. Es decir, seleccionamos la respuesta del chatbot que tiene la mayor probabilidad de ser seleccionada en cada paso de tiempo.

Hagamos un código para chatear con nuestra IA usando una búsqueda codiciosa:

# chatting 5 times with greedy search
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        pad_token_id=tokenizer.eos_token_id,
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Expliquemos el núcleo de este código:

• Primero tomamos información del usuario para chatear.
• Codificamos el texto para input_idsusar el tokenizador DialoGPT, también agregamos el final del token de cadena y lo devolvemos como un tensor de Pytorch.
• Si esta es la primera vez que chateamos con el bot, alimentaremos directamente input_idsnuestro modelo durante una generación. De lo contrario, agregamos el historial de chat usando la concatenación con la ayuda del torch.cat()método.
• Después de eso, usamos el model.generate()método para generar la respuesta del chatbot.
• Por último, como la salida devuelta también es una secuencia tokenizada, decodificamos la secuencia usando tokenizer.decode()y configuramos skip_special_tokenspara Trueasegurarnos de que no veamos ningún token especial molesto como <|endoftext|>. Además, dado que el modelo devuelve la secuencia completa, omitimos el historial de chat anterior e imprimimos solo la respuesta del chatbot recién generada.

A continuación se muestra una discusión de muestra con el bot:

>> You:How can you be rich so quickly?
DialoGPT: I'm not rich, I'm just a rich man.
>> You:Ok, you don't make sense, how old are you ?
DialoGPT: I'm not a rich man, I'm just a rich man.
>> You:How can you be happy ?
DialoGPT: I'm not happy, I'm just a rich man.
>> You:But they say money buys happiness!
DialoGPT: Money buys happiness, but not happiness itself.
>> You:You don't make sense again!
DialoGPT: I'm not happy, I'm just a rich man.

Verá que el modelo repite muchas respuestas, ya que estas son la probabilidad más alta y lo elige cada vez.

De forma predeterminada, model.generate()utiliza un algoritmo de búsqueda codicioso cuando no se establecen otros parámetros; en las siguientes secciones, agregaremos algunos argumentos a este método para ver si podemos mejorar la generación.

Generación de respuestas con Beam Search

La búsqueda por haz nos permite reducir el riesgo de perder secuencias de alta probabilidad al mantener las num_beamshipótesis más probables en cada paso de tiempo y luego tomar las secuencias que tienen la probabilidad general más alta, el siguiente código generará respuestas de chatbot con búsqueda de haz:

# chatting 5 times with beam search
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        num_beams=3,
        early_stopping=True,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Cuando se ajusta num_beamsa 3de model.generate()método, a continuación, vamos a seleccionar 3 palabras en cada paso de tiempo y desarrollarlas para encontrar la más alta probabilidad global de la secuencia, el establecimiento num_beamsde 1 es la misma que la búsqueda codiciosa.

A continuación se muestra una discusión de muestra con el chatbot usando la búsqueda de haz:

>> You:How can I be rich?
DialoGPT: You can't.
>> You:Why not?
DialoGPT: Because you can't.
>> You:Then how am I supposed to live well?
DialoGPT: You can't.
>> You:So basically, you want me to be a poor guy?
DialoGPT: No, I want you to be a rich guy.
>> You:Ok how to be rich, then?
DialoGPT: I don't know.

Generando respuestas con muestreo

En las secciones anteriores, usamos la búsqueda de rayos y codiciosos para generar la secuencia de probabilidad más alta. Eso es genial para tareas como la traducción automática o el resumen de texto donde el resultado es predecible. Sin embargo, no es la mejor opción para una generación abierta como en los chatbots.

Para una mejor generación, necesitamos introducir algo de aleatoriedad donde muestreamos de una amplia gama de secuencias candidatas basadas en probabilidades:

# chatting 5 times with sampling
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_k=0,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Esta vez, establece do_sampleque Truepara el muestreo, y nos pusimos top_ka 0lo que indica que estamos seleccionando todas las probabilidades posibles, vamos a discutir más adelante top_kparámetro.

Aquí hay un chat con estos parámetros:

>> You:Hello, how can I be rich?
DialoGPT: You can't
>> You:Really? then how others get rich?
DialoGPT: They work as money artists
>> You:How to be a money artist?
DialoGPT: I don't know, I don't have enough knowledge on artists
>> You:Alright, thank you!
DialoGPT: No problem, if you need tips pm me.
>> You:Great, you're my savior!
DialoGPT: Thank you for helping a stranger out like me!

Claramente hay algunas mejoras. Sin embargo, el muestreo en una amplia lista de secuencias con bajas probabilidades puede conducir a una generación aleatoria (como se ve en la última oración).

Para mejorarlo aún más, podemos:

• temperatureDisminuir el muestreo , eso nos ayuda a disminuir la probabilidad de elegir palabras de baja probabilidad y aumentar la probabilidad de elegir palabras de alta probabilidad.
• Utilice el muestreo de Top-k en lugar de seleccionar todas las ocurrencias probables, esto nos ayudará a descartar palabras de baja probabilidad para que no sean seleccionadas.

# chatting 5 times with Top K sampling & tweaking temperature
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_k=100,
        temperature=0.75,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Ahora, configuramos top_kpara 100muestrear las 100palabras principales ordenadas de forma descendente por probabilidad. También establecemos temperatureen 0.75(el valor predeterminado es 1.0) para brindar una mayor probabilidad de elegir palabras de alta probabilidad, establecer la temperatura en 0.0es lo mismo que la búsqueda codiciosa, establecerla en infinito es lo mismo que completamente aleatorio.

Aquí hay una discusión con estos parámetros:

>> You:How can I get rich?
DialoGPT: I'd also like to know this.
>> You:So you don't know it?
DialoGPT: I know it, but it doesn't matter.
>> You:Really? money matters, man!
DialoGPT: Well, money is money and I gotta get my money.
>> You:Yeah how to get them?
DialoGPT: Well, that's a question. I'd like to know more, just for curiosity's sake, to know what other people have said about how I should get rich.
>> You:Some robots like you told me you have to buy gold, silver or stocks.
DialoGPT: I think my wallet is empty.

Como se puede ver, es mucho mejor ahora, no dude en modificar temperaturey top_kparámetros y ver si puede mejorarlo.

Muestreo de núcleos

El muestreo de núcleo o muestreo de Top-p elige entre las palabras más pequeñas posibles cuya probabilidad acumulada excede el parámetro pque establecimos.

A continuación se muestra un ejemplo con el muestreo de Top-p:

# chatting 5 times with nucleus sampling & tweaking temperature
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_p=0.95,
        top_k=0,
        temperature=0.75,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the output
    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    print(f"DialoGPT: {output}")

Configuramos top_kpara 0deshabilitar el muestreo de Top-k, pero puede usar ambos métodos, lo que tiende a funcionar mejor. Aquí hay un chat:

>> You:How can I be rich fast?
DialoGPT: Inflation and free money
>> You:lol, but that takes time, I need fast money.
DialoGPT: Your post makes no sense. You just need a lot of money to be rich fast
>> You:Ok how to get a lot of money?
DialoGPT: Buy gold and then trade it for money.
>> You:Hmm, I got your point, thanks.
DialoGPT: No problem. You have to have money to buy gold, but its only good for trading it.
>> You:Yeah, I understand, thank you so much.
DialoGPT: Ok, let me know if you need help

Ahora el chatbot claramente tiene sentido en muchos casos.

Ahora agreguemos algo de código para generar más de una respuesta de chatbot, y luego elegimos qué respuesta incluir en la siguiente entrada:

# chatting 5 times with nucleus & top-k sampling & tweaking temperature & multiple
# sentences
for step in range(5):
    # take user input
    text = input(">> You:")
    # encode the input and add end of string token
    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
    # concatenate new user input with chat history (if there is)
    bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
    # generate a bot response
    chat_history_ids_list = model.generate(
        bot_input_ids,
        max_length=1000,
        do_sample=True,
        top_p=0.95,
        top_k=50,
        temperature=0.75,
        num_return_sequences=5,
        pad_token_id=tokenizer.eos_token_id
    )
    #print the outputs
    for i in range(len(chat_history_ids_list)):
      output = tokenizer.decode(chat_history_ids_list[i][bot_input_ids.shape[-1]:], skip_special_tokens=True)
      print(f"DialoGPT {i}: {output}")
    choice_index = int(input("Choose the response you want for the next input: "))
    chat_history_ids = torch.unsqueeze(chat_history_ids_list[choice_index], dim=0)

He configurado num_return_sequencespara 5devolver 5 oraciones a la vez, tenemos que elegir la que se incluirá en la siguiente secuencia. Así es como fue:

>> You:Hello, friend.
DialoGPT 0: Hey there!
DialoGPT 1: I am your friend, and I am also your friend.
DialoGPT 2: Hello. :
DialoGPT 3: Hiya! How are you?
DialoGPT 4: Hello. I like your username.
Choose the response you want for the next input: 3
>> You:Fine, thanks, what about you?
DialoGPT 0: Good. Not much to do.
DialoGPT 1: Not much, you?
DialoGPT 2: Fine, thanks.
DialoGPT 3: I'm okay, thank you for asking.
DialoGPT 4: I'm fine too.
Choose the response you want for the next input: 2
>> You:Can you tell me how can I get rich quickly?
DialoGPT 0: Yes, I can!
DialoGPT 1: Yes, just look up how to get rich on Google.
DialoGPT 2: Just keep doing your thing. You'll get rich.
DialoGPT 3: No, I can't.
DialoGPT 4: no, i have no money
Choose the response you want for the next input: 1
>> You:Yeah, but I thought you have an answer...
DialoGPT 0: I'm getting married in the morning.
DialoGPT 1: How do you get rich?
DialoGPT 2: I think there are many good answers, but I could send you one, if you want.
DialoGPT 3: I will, thanks!
DialoGPT 4: I thought you have an answer.
Choose the response you want for the next input: 2
>> You:Yes, please send me.
DialoGPT 0: You can send me link.
DialoGPT 1: I am very interested
DialoGPT 2: I sent you a PM
DialoGPT 3: I'll send you a PM
DialoGPT 4: I am always interested in new ideas.
Choose the response you want for the next input: 2

Conclusión

Y ahí lo tienes, espero que este tutorial te haya ayudado a generar texto en DialoGPT y modelos similares. Para obtener más información sobre cómo generar texto, le recomiendo que lea la guía Cómo generar texto con Transformers .

Te dejo ajustando los parámetros para ver si puedes hacer que el bot funcione mejor.

Además, puede combinar esto con tutoriales de texto a voz y de voz a texto para crear un asistente virtual como Alexa , Siri , Cortana , etc.

#python #chatbot #ai 

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Buddha Community

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

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How This AI-Enabled Chatbot Radically Transformed Cancer Care Amid Pandemic

The critical industry that has been massively impacted by the pandemic is the healthcare sector; however, AI’s involvement has helped the industry weather the pandemic storm. The AI applications by companies bring back patients’ lives from the edge and improve diagnostics and treatment and help healthcare providers make informed decisions quickly. One such application has been developed by Hospido, India’s first holistic cancer care platform with which cancer patients can get the right treatment advice from India’s best cancer doctors, without visiting hospitals amid pandemic.

The pandemic lockdown has forced many people, including cancer patients, to avoid hospitals and discontinue their treatment due to coronavirus risk. And this is what triggered Karan Chopra, the founder of Hospido to bring out quality healthcare to these cancer patients through telemedicine and satellite treatments centres for providing the right treatment at the right time. In this article, Analytics India Magazine, got in touch with him to understand how this startup revolutionised cancer carer amid pandemic.

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Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

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Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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AI Chatbots for Business: Why You Need One Now!

It’s said that Artificial Intelligence will be just as smart as humans by 2050. Experts like Ray Kurzweil have even predicted that we’ll achieve a technological singularity by 2045.

From that point on, it’s believed that AI will start inventing Nobel Prize-winning inventions every 5 minutes. Granted it’s gonna be out of our control, but hey, at least we’ll see a revolutionary breakthrough.

We may think that these claims are outlandish and ridiculous, but if someone were to tell me in the 70s that there will be self-driving cars in the future, I would’ve wanted to smoke whatever they were smoking.

But guess what, here we are in 2020, and Tesla already has their self-driving cars on the roads right now. And these were all recently developed technologies. Did you know that the first chatbot was actually launched in 1966?

Features of AI Chatbots

Why Do You Need an AI Chatbot?

Chatbots Across Various Industries

Wrapping Up

#ai-chatbot #what-is-a-chatbot #chatbot-online #chatbot #chatbot-website #facebook-chatbot #google-chatbot #best-chatbot